Introduction

Why are some species rare and others common? Why does a species occur at low population densities in some places and at high densities in others? What factors cause fluctuations in a species' abundance? These are crucial questions. To provide complete answers for even a single species in a single location, we might need, ideally, a knowledge of physicochemical conditions, the level of resources available, the organism's life cycle and the influence of competitors, predators, parasites, etc., as well as an understanding of how all these things influence abundance through their effects on the rates of birth, death and movement. In previous chapters, we have examined each of these topics separately. We now bring them together to see how we might discover which factors actually matter in particular examples.

The raw material for the study of abundance is usually some estimate of population size. In its crudest form, this consists of a simple count. But this can hide vital information. As an example, picture three human populations containing identical numbers of individuals. One of these is an old people's residential area, the second is a population of young children, and the third is a population of mixed age and sex. No amount of attempted correlation with factors outside the population would reveal that the first was doomed to extinction (unless maintained by immigration), the second would grow fast but only after a delay, and the third would continue to grow steadily. More detailed studies, therefore, involve recognizing individuals of different age, sex, size and dominance and even distinguishing genetic variants.

Ecologists usually have to deal with estimates of abundance that are deficient. First, data may be misleading unless sampling is adequate over both space and time, and adequacy of either usually requires great commitment of time and money. The lifetime of investigators, the hurry to produce publishable work and the short tenure of most research programs all deter individuals from even starting to conduct studies over extended periods of time. Moreover, as knowledge about populations grows, so the number of attributes of interest grows and changes; every study risks being out of date almost as soon as it begins. In particular, it is usually a technically formidable task to follow individuals in a population throughout their lives. Often, a crucial stage in the life cycle is hidden from view - baby rabbits within their warrens or seeds in the soil. It is possible to mark birds with numbered leg rings, roving carnivores with radiotransmitters or seeds with radioactive isotopes, but the species and the numbers that can be studied in this way are severely limited.

A large part of population theory depends on the relatively few exceptions where logistical difficulties have been overcome (Taylor, 1987). In fact, most of the really long-term or geographically extensive studies of abundance have been made of organisms of economic importance such as fur-bearing animals, game birds and pests, or the furry and feathered favorites of amateur naturalists. Insofar as generalizations emerge, we should treat them with great caution.

14.1.1 Correlation, causation and experimentation

Abundance data may be used to establish correlations with external factors (e.g. the weather) or correlations between features within the abundance data themselves (e.g. correlating numbers present in the spring with those present in the fall). Correlations may be used to predict the future. For example, high intensities of the disease 'late blight' in the canopy of potato crops usually occur 15-22 days after a period in which the minimum counting is not enough estimates are usually deficient studied species may not be typical temperature is not less than 10°C and the relative humidity is more than 75% for two consecutive days. Such a correlation may alert the grower to the need for protective spraying.

Correlations may also be used to suggest, although not to prove, causal relationships. For example, a correlation may be demonstrated between the size of a population and its growth rate. The correlation may hint that it is the size of the population itself that causes the growth rate to change, but, ultimately, 'cause' requires a mechanism. It may be that when the population is high many individuals starve to death, or fail to reproduce, or become aggressive and drive out the weaker members.

In particular, as we have remarked previously, many of the studies that we discuss in this and other chapters have been concerned to detect 'density-dependent' processes, as if density itself is the cause of changes in birth rates and death rates in a population. But this will rarely (if ever) be the case: organisms do not detect and respond to the density of their populations. They usually respond to a shortage of resources caused by neighbors or to aggression. We may not be able to identify which individuals have been responsible for the harm done to others, but we need continually to remember that 'density' is often an abstraction that conceals what the world is like as experienced in the lives of real organisms.

Observing directly what is happening to the individuals may suggest more strongly still what causes a change in overall abundance. Incorporating observations on individuals into mathematical models of populations, and finding that the model population behaves like the real population, may also provide strong support for a particular hypothesis. But often, the acid test comes when it is possible to carry out a field experiment or manipulation. If we suspect that predators or competitors determine the size of a population, we can ask what happens if we remove them. If we suspect that a resource limits the size of a population, we can add more of it. Besides indicating the adequacy of our hypotheses, the results of such experiments may show that we ourselves have the power to determine a population's size: to reduce the density of a pest or weed, or to increase the density of an endangered species. Ecology becomes a predictive science when it can forecast the future: it becomes a management science when it can determine the future.

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